CN103903101B - A kind of General Aviation multi-source information supervising platform and method thereof - Google Patents

Abstract

The present invention relates to a kind of General Aviation multi-source information supervising platform and method thereof, the target of acquisition sensor detection is classified, carry out association judgement and data fusion to the information that multisensor is observed simultaneously by fuzzy clustering algorithm, the data precision after fusion is better than the accuracy of observation of any sensor.When after data fusion, in conjunction with outside integrated information, data analysis is carried out to situation.If exist abnormal, database module is automatically enrolled these data and is produced early warning information.Measures of effectiveness module in the present invention, can carry out running state monitoring and measures of effectiveness to acquisition sensor in real time, by the power range dynamic conditioning of human-computer interaction module to system looks sensor, keeps the maximization of platform usefulness.The present invention, by carrying out data correlation judgement and data fusion, measures of effectiveness and Study on Trend to the detection data of many acquisition sensors, obtains the empty feelings of entirety of monitoring range, realizes system-level supervision.

Description

A kind of General Aviation multi-source information supervising platform and method thereof

Technical field

The present invention relates to a kind of information monitoring platform and method thereof, particularly relate to a kind of General Aviation multi-source information supervising platform and method thereof.

Background technology

Common aero vehicle monitor management platform carries out monitoring for General Aviation low altitude airspace aircraft, manages and distribute the unified platform of information service.Due to common aero vehicle there is aircraft wide variety, ceiling is low, speed is slow, the nonstandard problem of airborne equipment, needs employing surveillance equipment, cooperative surveillance equipment, aerological sounding equipment multiclass sensor jointly to monitor.The means that each monitoring equipment of existing civil aviaton air traffic control is independently supervised are more single, and General Aviation monitor management needs to carry out data processing to multi-data source information, just can draw unified, real-time, the accurate empty feelings in overlay area, therefore research and develop a kind of General Aviation multi-source information supervising platform and method is very necessary.

Summary of the invention

The object of the present invention is to provide a kind of General Aviation multi-source information supervising platform, solve multisensor in General Aviation Flight monitor management and jointly detect Target Splitting and the redundancy phenomena of appearance.

To achieve these goals, the invention provides a kind of General Aviation multi-source information supervising platform, comprise data fusion module, resource management module, database module and human-computer interaction module, wherein:

Described data fusion module comprises data correlation Fusion Module, Study on Trend module and measures of effectiveness module;

Described data correlation Fusion Module, carries out association judgement and data fusion to the track data that some acquisition sensors detect by fuzzy clustering algorithm, draws overall empty feelings information;

Described Study on Trend module, carries out Study on Trend by empty for described entirety feelings information in conjunction with outside integrated information, if the data of noting abnormalities, is recorded in described database module;

Described measures of effectiveness module, carries out running status real time monitoring to described some acquisition sensors, assesses platform and integrally usefulness;

By sensor detectivity, sensor antijamming capability, sensing system platform property and sensor anti-low latitude ability are resolved into several component further, then weight calculation is carried out to these components draw platform measures of effectiveness result; Weighing computation method is specific as follows:

According to evaluation module, component is divided, determine that set of factors is: detectivity U ₁{ low clearance area coverage coefficient, key area coverage coefficient, warning region coverage coefficient, guidance field coverage coefficient }; Anti-low latitude ability U ₂{ sensor type, sensor system }; System performance U ₃{ benefit coefficient, system performance, system operating mode, coefficient of frequency }; U ₄antijamming capability { spatial domain overlap coefficient, frequency overlap coefficient, polarization factor, the signal type factor, the signal handling capacity factor, single-sensor antijamming capability }, uses matrix U ₁={ u ₁, u ₂, u ₃, u ₄; U ₂={ u ₅, u ₆; U ₃={ u ₇, u ₈, u ₉, u ₁₀; U ₄={ u ₁₁, u ₁₂, u ₁₃, u ₁₄, u ₁₅, u ₁₆represent;

If U ₁, U ₂, U ₃, U ₄the each self-corresponding weight sets matrix of set of factors is: A ₁={ a ₁₁, a ₁₂, a ₁₃, a ₁₄; A ₂={ a ₂₁, a ₂₂; A ₃={ a ₃₁, a ₃₂, a ₃₃, a ₃₄; A ₄={ a ₄₁, a ₄₂, a ₄₃, a ₄₄, a ₄₅, a ₄₆, and $\underset{j}{\Σ}{a}_{ij}=1,i=1,2,3,4;$

If Effectiveness Evaluation result set is { excellent, good, in, better, poor }, with matrix V={ v ₁, v ₂... v ₅represent;

The degree of membership of each component on measures of effectiveness result set V constitutes fuzzy relationship matrix r=(μ (u)) of this evaluation process U to V _{i × J}, wherein μ is fuzzification function, and u is each component, and I is factor number in set of factors, and J is result set result number;

Finally draw Effectiveness Evaluation computing formula: B=A ο R=(b ₁, b ₂, b ₃, b ₄, b ₅), b _j(j=1,2 ... 5) larger, illustrate that this system effectiveness is under the jurisdiction of v _jthe degree of Efficacy Results collection is larger;

Described resource management module comprises sensor management module and comprehensive analysis module;

Described sensor management module, completes the condition managing to described some acquisition sensors, condition monitoring and task scheduling; Described comprehensive analysis module, the association of the track data of comprehensive analysis measures of effectiveness result, the detection of described some acquisition sensor is merged and described some acquisition sensor running state information, forms overall empty feelings information, the coverage information of overall detect effi-ciency and resource health status information;

Described database module, is connected with data fusion module, resource management module and human-computer interaction module, automatically enrolls abnormal data, inquires about and playback;

Described human-computer interaction module, connect described data fusion module and described resource management module, show overall empty feelings information, the coverage information of overall detect effi-ciency and resource health status information, send when receiving abnormal data and threaten report and early warning and by the power range dynamic conditioning of described sensor management module to some acquisition sensors.

Preferably, described human-computer interaction module carries out switching display to the coverage information of entirety empty feelings information, overall detect effi-ciency and resource health status information.

Preferably, described outside integrated information is weather information, the geography information of outside geographical information platform collection and the blank pipe information of outside blank pipe platform collection that outside weather information platform gathers.

Preferably, also comprise information service module, empty for entirety feelings information is carried out data processing, navigation, meteorological and early warning category information service are provided.

Preferably, the empty feelings information of described entirety comprises target type and target status information.

Preferably, described target status information comprises flight number, invasion time, the speed of a ship or plane, course, flying height, flying speed and landing event information.

To achieve these goals, present invention also offers a kind of General Aviation multi-source information monitoring and managing method, comprise the following steps:

By fuzzy clustering algorithm, association judgement and data fusion are carried out to the track data that some acquisition sensors detect, draws overall empty feelings information;

Empty for described entirety feelings information is carried out Study on Trend in conjunction with outside integrated information;

Running status real time monitoring is carried out to described acquisition sensor, platform and integrally usefulness is assessed;

Condition managing, condition monitoring and task scheduling are carried out to described some acquisition sensors;

The association fusion results of the track data of comprehensive analysis measures of effectiveness result, described some acquisition sensor detections and described some acquisition sensor running state information, form usefulness coverage information and the resource health status information of comprehensive empty feelings information and overall detection;

The overall empty feelings information of real-time display, the coverage information of overall detect effi-ciency and resource health status information;

During the abnormal data occurred in platform, abnormal data enrolled automatically, inquire about and playback, and when receiving abnormal data, send threat report and early warning, and the power range dynamic conditioning to some acquisition sensors.

Preferably, the track data of detection carries out associating and to judge and the step of data fusion also comprises:

Calibrate pre-service when a. the target that each acquisition sensor detects being carried out sky, obtain the tenacious tracking track of each sensor detection information;

B. Registration of Measuring Data is carried out by time stamp alignment so;

C. fuzzy clustering algorithm is adopted to carry out data correlation judgement and data fusion;

D. overall empty feelings information is drawn.

Preferably, the overall empty feelings information of display, the coverage information of overall detect effi-ciency and resource health status information is switched.

Preferably, described outside integrated information is weather information, the geography information of outside Geographic Information System collection and the blank pipe information of outside air traffic control system collection that outside weather infosystem gathers.

Preferably, the empty feelings information of described entirety comprises target type and target status information.

Preferably, described target status information comprises flight number, invasion time, the speed of a ship or plane, course, flying height and landing event information.

Preferably, also comprise and empty for entirety feelings information is carried out data processing, navigation, meteorological and early warning category information service are provided.

The present invention, owing to adopting above technical scheme, compared with prior art, has following advantage and good effect:

1) the switching display of many middle information interfaces and the empty feelings of entirety can be realized in human-computer interaction module of the present invention, only need a display terminal, namely meet General Aviation monitor management simplified design demand;

2) the fuzzy clustering data blending algorithm of the present invention's design, the result precision of data fusion is better than merging front single acquisition sensor data precision, solves multisensor in General Aviation Flight monitor management and jointly detects Target Splitting and the redundancy phenomena of appearance;

3) the present invention designs Situation Assessment module, can carry out running state monitoring and measures of effectiveness in real time to acquisition sensor, by the power range dynamic conditioning of human-computer interaction module to system looks sensor, and keeping system realizes maximal efficiency.

4) the sensor management module in the present invention is to the described acquisition sensor running status dynamic-configuration management of monitoring, and the running status realizing multisensor monitors configuration in real time.

Accompanying drawing explanation

Fig. 1 is the general diagram of a kind of General Aviation multi-source information supervising platform provided by the invention;

Fig. 2 is the schematic diagram of data correlation Fusion Module received data in the embodiment of the present invention;

Fig. 3 is the component schematic diagram of measures of effectiveness in the embodiment of the present invention;

Fig. 4 is the process flow diagram of a kind of General Aviation multi-source information monitoring and managing method provided by the invention;

Fig. 5 is the process flow diagram that in the embodiment of the present invention, track data carries out associating judgement and data fusion;

Fig. 6 is the course of work block diagram of Study on Trend module in the embodiment of the present invention.

Embodiment

The present invention is further illustrated with reference to the accompanying drawings with specific embodiment.

Embodiment 1

As illustrated in the accompanying drawings from 1 to 3, a kind of General Aviation multi-source information supervising platform provided by the invention, comprise data fusion module 11, resource management module 12, database module 13, human-computer interaction module 14 and information service module 15, wherein: data fusion module 11 comprises data correlation Fusion Module 111, Study on Trend module 112 and measures of effectiveness module 113; Data correlation Fusion Module 111, carry out association judgement and data fusion to the track data that some acquisition sensors 2 detect by fuzzy clustering algorithm, draw overall empty feelings information, overall empty feelings information comprises target type and target status information.Target status information comprises flight number, invasion time, the speed of a ship or plane, course, flying height, flying speed and landing event information.Target type comprises commercial airliner, common aero vehicle etc.

Be specifically described fuzzy clustering algorithm below, acquisition sensor is generally radar, but is not limited to this.As shown in Figure 2, there to be 2 different radars jointly to observe three objectives 21,22,23 be example, this is not limited to:

The principle of fuzzy clustering algorithm utilizes the uncertainty of observation data (i.e. ambiguity) that n the Monitoring Data that at a time t obtains is distributed to m flight path, describes the similarity degree of m flight path with membership function.

We adopt R _ijrepresent the attribute of the detection of a target, wherein, i=1,2, j=1,2.Present problem judges R ₁₁, R ₁₂, R ₂₁, R ₂₂whether there is the flight path belonging to same target.This problem is considered as to the two-value Hypothesis Testing Problem of two radars 24,25: use H ₁representing two flight paths is same targetpaths, H ₀represent the flight path that 2 flight paths are different targets, that is:

$H=\left\{\begin{array}{c}1,H_1\\ 0,H_0\end{array}\right.---\left(1\right)$

The statistical distance of definition 2 flight paths is:

$d_{ij}=\left\{\begin{array}{c}\left|\right|R_j-R_i|{|}^2,i\≠j\\ \left|\right|{\mathrm{\Δ}}_i|{|}^2,i=j\end{array}\right.---\left(2\right)$

Fuzzy clustering algorithm is utilized to determine the best { d _ijsimilarity matrix between (i, j=1,2) element $U=\left(\begin{array}{c}{u}_{11},u_{12}\\ u_{21},u_{22}\end{array}\right).$

Wherein,

$u_{11}=\frac{{\left({\mathrm{\Δ}}_1{{\mathrm{\Δ}}^{\′}}_1\right)}^{1/(1-m)}}{{\left({\mathrm{\Δ}}_1{{\mathrm{\Δ}}^{\′}}_1\right)}^{1/(1-m)}+{\left({\left(R_1-R_2\right)}^{\′}\right(R_1-R_2\left)\right)}^{1/(1-m)}},---\left(3\right)$

$u_{12}=\frac{{\left({\left(R_1-R_2\right)}^{\′}\right(R_1-R_2\left)\right)}^{1/(1-m)}}{{\left({\mathrm{\Δ}}_2{{\mathrm{\Δ}}^{\′}}_2\right)}^{1/(1-m)}+{\left({\left(R_2-R_1\right)}^{\′}\right(R_2-R_1\left)\right)}^{1/(1-m)}},---\left(4\right)$

$u_{21}=\frac{{\left({\left(R_2-R_1\right)}^{\′}\right(R_2-R_1\left)\right)}^{1/(1-m)}}{{\left({\mathrm{\Δ}}_2{{\mathrm{\Δ}}^{\′}}_2\right)}^{1/(1-m)}+{\left({\left(R_1-R_2\right)}^{\′}\right(R_1-R_2\left)\right)}^{1/(1-m)}},---\left(5\right)$

$u_{22}=\frac{{\left({\mathrm{\Δ}}_2{{\mathrm{\Δ}}^{\′}}_2\right)}^{1/(1-m)}}{{\left({\mathrm{\Δ}}_2{{\mathrm{\Δ}}^{\′}}_2\right)}^{1/(1-m)}+{\left({\left(R_2-R_1\right)}^{\′}\right(R_2-R_1\left)\right)}^{1/(1-m)}},---\left(6\right)$

Wherein, m is weight factor, and usual span is 1 ~ 5.

Interrelated decision D _ijusually determine according to minimum precision radar.That is,

$D_{ij}=\left\{\begin{array}{c}1,u_{12}>u_{22}\\ 0,u_{12}<u_{22}\end{array}\right.---\left(7\right)$

In formula, D _ij=1 represents that 2 flight paths are under the jurisdiction of the same detection of a target; D _ij=0 represents that 2 flight paths are under the jurisdiction of different target.

For 2 flight paths being under the jurisdiction of same target, can Track Fusion be carried out, obtain the new flight path that precision is higher:

$R_f=\frac{\underset{k=k_1}{\overset{k_s}{\&Sigma;}}{R}_{ij}{u}_{kk}}{\underset{k=k_1}{\overset{k_2}{\&Sigma;}}{u}_{kk}}---\left(8\right)$

Wherein, k _sup=Max _k{ u _kk, k=k ₁, k ₂...., k _s.

Study on Trend module 112, by empty for entirety feelings information, the i.e. result of data fusion, Study on Trend is carried out in conjunction with outside integrated information, impended by outside air traffic control system after forming situation report and assess and countermeasure analysis, thus the grade that impends judges, if the data of noting abnormalities, is recorded in database module 13, and send threat report and early warning to human-computer interaction module 14, see Fig. 6.

Outside integrated information comprises weather information, the geography information of outside Geographic Information System 202 collection and the blank pipe information of outside air traffic control system 203 collection that outside weather infosystem 201 gathers.

Measures of effectiveness module 113, carries out running status real time monitoring to some acquisition sensors, assesses platform and integrally usefulness.By sensor detectivity, sensor antijamming capability, sensing system platform property and sensor anti-low latitude ability are resolved into several component further, several component as shown in Figure 3, then carries out weight calculation to these components and draws platform measures of effectiveness result.

Weighing computation method is specific as follows:

According to evaluation module, component is divided, determine that set of factors is: detectivity U ₁{ low clearance area coverage coefficient, key area coverage coefficient, warning region coverage coefficient, guidance field coverage coefficient }; Anti-low latitude ability U ₂{ sensor type, sensor system }; System performance U ₃{ benefit coefficient, system performance, system operating mode, coefficient of frequency }; U ₄antijamming capability { spatial domain overlap coefficient, frequency overlap coefficient, polarization factor, the signal type factor, the signal handling capacity factor, single-sensor antijamming capability }, uses matrix U ₁={ u ₁, u ₂, u ₃, u ₄; U ₂={ u ₅, u ₆; U ₃={ u ₇, u ₈, u ₉, u ₁₀; U ₄={ u ₁₁, u ₁₂, u ₁₃, u ₁₄, u ₁₅, u ₁₆represent.

If U ₁, U ₂, U ₃, U ₄the each self-corresponding weight sets matrix of set of factors is: A ₁={ a ₁₁, a ₁₂, a ₁₃, a ₁₄; A ₂={ a ₂₁, a ₂₂; A ₃={ a ₃₁, a ₃₂, a ₃₃, a ₃₄; A ₄={ a ₄₁, a ₄₂, a ₄₃, a ₄₄, a ₄₅, a ₄₆, and $\underset{j}{\&Sigma;}{a}_{ij}=1,i=1,2,3,4.$

If Effectiveness Evaluation result set is { excellent, good, in, better, poor }, with matrix V={ v ₁, v ₂... v ₅represent.

The degree of membership of each component on measures of effectiveness result set V constitutes fuzzy relationship matrix r=(μ (u)) of this evaluation process U to V _{i × J}, wherein μ is fuzzification function, and u is each component, and I is factor number in set of factors, and J is result set result number.

Finally draw Effectiveness Evaluation computing formula: B=A ο R=(b ₁, b ₂, b ₃, b ₄, b ₅), b _j(j=1,2 ... 5) larger, illustrate that this system effectiveness is under the jurisdiction of v _jthe degree of Efficacy Results collection is larger.

Resource management module 12 comprises sensor management module 121 and comprehensive analysis module 122; Sensor management module 121, completes the condition managing to some acquisition sensors, condition monitoring and task scheduling, the state of its acquisition sensor generally comprise off-line, online, make mistakes, mourn in silence.Comprehensive analysis module 122, the association of the track data of comprehensive analysis measures of effectiveness result, some acquisition sensors detection is merged and some acquisition sensor running state information, forms overall empty feelings information, the coverage information of overall detect effi-ciency and resource health status information.Database module 12 is connected with data fusion module 11, resource management module 12 and human-computer interaction module 14, automatically enrolls, inquires about and playback abnormal data.Human-computer interaction module 14, connection data Fusion Module 11 and resource management module 12, show overall empty feelings information, the coverage information of overall detect effi-ciency and resource health status information, send when receiving abnormal data and threaten report and early warning by the power range dynamic conditioning of sensor management module to some acquisition sensors, early warning type comprises meteorology, no-fly zone, deviated route etc.Human-computer interaction module 111 can carry out switching display to the coverage information of entirety empty feelings information, overall detect effi-ciency and resource health status information, and human-computer interaction module also can background superposition geography information and weather information when showing overall empty feelings information.Empty for entirety feelings information is carried out data processing by information service module 15, provides navigation, meteorological and early warning category information service.

In the present embodiment, hardware adopts general purpose PC, and processor host frequency is not less than 2.0GHz, and internal memory is not less than 4GBytes, and hard disk is not less than 100GBytes, possesses lan network interface.Software adopts Windows operating system, SQLserver database, VS2008 application software development platform, is not limited to this.

In sum, this platform is classified to the target that acquisition sensor detects, and carry out association judgement and data fusion to the information that many acquisition sensors are observed simultaneously by fuzzy clustering algorithm, the data precision after fusion is better than the accuracy of observation of any sensor.After total data merges, in conjunction with outside integrated information, data analysis is carried out to situation.If exist abnormal, platform database is automatically enrolled these data and is produced early warning information.Measures of effectiveness module can carry out running state monitoring and measures of effectiveness to acquisition sensor in real time, by the power range dynamic conditioning of human-computer interaction module to system looks sensor, and keeping system realizes maximal efficiency.This platform both can monitor separately the detection data of any one acquisition sensor, also by carrying out data correlation judgement and data fusion, Situation Assessment and Study on Trend to the detection data information of many acquisition sensors, obtain the empty feelings of entirety of monitoring range, realize system-level supervision, and navigation, meteorological and early warning category information service can be derived.This platform is particularly useful for monitoring the aircraft of low altitude airspace, managing and distribute information service.

Embodiment 2

As shown in accompanying drawing 4-5, be the process flow diagram of a kind of General Aviation multi-source information monitoring and managing method provided by the invention, the method is realized by a kind of General Aviation multi-source information supervising platform provided by the invention, makes an explanation to this method in detail below.Comprise the following steps:

By fuzzy clustering algorithm, association judgement and data fusion are carried out to the track data that some acquisition sensors detect, draws overall empty feelings information.As shown in Figure 5, wherein specifically comprise:

Calibrate pre-service when a. the target that each acquisition sensor detects being carried out sky, obtain each sensor detection information tenacious tracking track.

B. Registration of Measuring Data is carried out by time stamp alignment so.

C. adopt fuzzy clustering algorithm to carry out data correlation judgement and data fusion, the new flight path that precision is higher can be obtained.Be specifically described fuzzy clustering algorithm below, acquisition sensor is generally radar, but is not limited to this.As shown in Figure 2, there to be 2 different radars jointly to observe three objectives 21,22,23 be example, this is not limited to:

The principle of fuzzy clustering algorithm utilizes the uncertainty of Monitoring Data (i.e. ambiguity) that n the Monitoring Data that at a time t obtains is distributed to m flight path, describes the similarity degree of m flight path with membership function.

We adopt R _ijrepresent the attribute of the detection of a target, wherein, i=1,2, j=1,2.Present problem judges R ₁₁, R ₁₂, R ₂₁, R ₂₂whether there is the flight path belonging to same target.This problem is considered as to the two-value Hypothesis Testing Problem of two radars 24,25: use H ₁representing two flight paths is same targetpaths, H ₀represent the flight path that 2 flight paths are different targets, that is:

$H=\left\{\begin{array}{c}1,H_1\\ 0,H_0\end{array}\right.---\left(1\right)$

The statistical distance of definition 2 flight paths is:

$d_{ij}=\left\{\begin{array}{c}\left|\right|R_j-R_i|{|}^2,i\&NotEqual;j\\ \left|\right|{\mathrm{\&Delta;}}_i|{|}^2,i=j\end{array}\right.---\left(2\right)$

Fuzzy clustering algorithm is utilized to determine the best { d _ijsimilarity matrix between (i, j=1,2) element $U=\left(\begin{array}{c}{u}_{11},u_{12}\\ u_{21},u_{22}\end{array}\right).$

Wherein,

$u_{11}=\frac{{\left({\mathrm{\&Delta;}}_1{{\mathrm{\&Delta;}}^{\&prime;}}_1\right)}^{1/(1-m)}}{{\left({\mathrm{\&Delta;}}_1{{\mathrm{\&Delta;}}^{\&prime;}}_1\right)}^{1/(1-m)}+{\left({\left(R_1-R_2\right)}^{\&prime;}\right(R_1-R_2\left)\right)}^{1/(1-m)}},---\left(3\right)$

$u_{12}=\frac{{\left({\left(R_1-R_2\right)}^{\&prime;}\right(R_1-R_2\left)\right)}^{1/(1-m)}}{{\left({\mathrm{\&Delta;}}_2{{\mathrm{\&Delta;}}^{\&prime;}}_2\right)}^{1/(1-m)}+{\left({\left(R_2-R_1\right)}^{\&prime;}\right(R_2-R_1\left)\right)}^{1/(1-m)}},---\left(4\right)$

$u_{21}=\frac{{\left({\left(R_2-R_1\right)}^{\&prime;}\right(R_2-R_1\left)\right)}^{1/(1-m)}}{{\left({\mathrm{\&Delta;}}_2{{\mathrm{\&Delta;}}^{\&prime;}}_2\right)}^{1/(1-m)}+{\left({\left(R_1-R_2\right)}^{\&prime;}\right(R_1-R_2\left)\right)}^{1/(1-m)}},---\left(5\right)$

$u_{22}=\frac{{\left({\mathrm{\&Delta;}}_2{{\mathrm{\&Delta;}}^{\&prime;}}_2\right)}^{1/(1-m)}}{{\left({\mathrm{\&Delta;}}_2{{\mathrm{\&Delta;}}^{\&prime;}}_2\right)}^{1/(1-m)}+{\left({\left(R_2-R_1\right)}^{\&prime;}\right(R_2-R_1\left)\right)}^{1/(1-m)}},---\left(6\right)$

Wherein, m is weight factor, and usual span is 1 ~ 5.

Interrelated decision D _ijusually determine according to minimum precision radar.That is,

$D_{ij}=\left\{\begin{array}{c}1,u_{12}>u_{22}\\ 0,u_{12}<u_{22}\end{array}\right.---\left(7\right)$

In formula, D _ij=1 represents that 2 flight paths are under the jurisdiction of the same detection of a target; D _ij=0 represents that 2 flight paths are under the jurisdiction of different target.

For 2 flight paths being under the jurisdiction of same target, can Track Fusion be carried out, obtain the new flight path that precision is higher:

$R_f=\frac{\underset{k=k_1}{\overset{k_s}{\&Sigma;}}{R}_{ij}{u}_{kk}}{\underset{k=k_1}{\overset{k_2}{\&Sigma;}}{u}_{kk}}---\left(8\right)$

Wherein, k _sup=Max _k{ u _kk, k=k ₁, k ₂...., k _s.

D. draw overall empty feelings information, namely draw target type and target status information.Target status information comprises flight number, invasion time, the speed of a ship or plane, course, flying height and landing event information; Target type comprises commercial airliner, common aero vehicle etc.

Empty for entirety feelings information is carried out Study on Trend in conjunction with outside integrated information.Outside integrated information comprises weather information, the geography information of outside Geographic Information System 202 collection and the blank pipe information of outside air traffic control system 203 collection that outside weather infosystem 201 gathers, impended by outside air traffic control system after forming situation report and assess and countermeasure analysis, thus the grade that impends judges, if the data of noting abnormalities, be recorded in database module 13, and send threat report and early warning to human-computer interaction module 14.

Running status real time monitoring is carried out to acquisition sensor, platform and integrally usefulness is assessed.By sensor detectivity, sensor antijamming capability, sensing system performance and sensor anti-low latitude ability are resolved into several component further, several component as shown in Figure 3, then carries out weight calculation to these components and draws platform measures of effectiveness result.

Weighing computation method is specific as follows:

According to evaluation module, component is divided, determine that set of factors is: detectivity U ₁{ low clearance area coverage coefficient, key area coverage coefficient, warning region coverage coefficient, guidance field coverage coefficient }; Anti-low latitude ability U ₂{ sensor type, sensor system }; System performance U ₃{ benefit coefficient, system performance, system operating mode, coefficient of frequency }; U ₄antijamming capability { spatial domain overlap coefficient, frequency overlap coefficient, polarization factor, the signal type factor, the signal handling capacity factor, single-sensor antijamming capability }, uses matrix U ₁={ u ₁, u ₂, u ₃, u ₄; U ₂={ u ₅, u ₆; U ₃={ u ₇, u ₈, u ₉, u ₁₀; U ₄={ u ₁₁, u ₁₂, u ₁₃, u ₁₄, u ₁₅, u ₁₆represent.

If U ₁, U ₂, U ₃, U ₄the each self-corresponding weight sets matrix of set of factors is: A ₁={ a ₁₁, a ₁₂, a ₁₃, a ₁₄; A ₂={ a ₂₁, a ₂₂; A ₃={ a ₃₁, a ₃₂, a ₃₃, a ₃₄; A ₄={ a ₄₁, a ₄₂, a ₄₃, a ₄₄, a ₄₅, a ₄₆, and $\underset{j}{\&Sigma;}{a}_{ij}=1,i=1,2,3,4.$

If Effectiveness Evaluation result set is { excellent, good, in, better, poor }, with matrix V={ v ₁, v ₂... v ₅represent.

The degree of membership of each component on measures of effectiveness result set V constitutes fuzzy relationship matrix r=(μ (u)) of this evaluation process U to V _{i × J}, wherein μ is fuzzification function, and u is each component, and I is factor number in set of factors, and J is result set result number.

Finally draw Effectiveness Evaluation computing formula: B=A ο R=(b ₁, b ₂, b ₃, b ₄, b ₅), b _j(j=1,2 ... 5) larger, illustrate that this system effectiveness is under the jurisdiction of v _jthe degree of Efficacy Results collection is larger.

Condition managing, condition monitoring and task scheduling are carried out to some acquisition sensors, the state of its acquisition sensor generally comprise off-line, online, make mistakes, mourn in silence.

The association fusion results of the track data of comprehensive analysis measures of effectiveness result, some acquisition sensors detection and some acquisition sensor running state information, form usefulness coverage information and the resource health status information of comprehensive empty feelings information and overall detection.

The overall empty feelings information of real-time display, the coverage information of overall detect effi-ciency and resource health status information.Switching display can be carried out to the coverage information of entirety empty feelings information, overall detect effi-ciency and resource health status information, also can to monitor separately an acquisition sensor.Also can background superposition geography information and weather information when showing overall empty feelings information.

During the abnormal data occurred in platform, by database module, abnormal data enrolled automatically, inquire about and playback, and when receiving abnormal data, send threat report and early warning, and the state dynamic conditioning to some acquisition sensors.Wherein, early warning type comprises meteorology, no-fly zone, deviated route etc.

Also empty for real-time entirety feelings information can be carried out data processing, navigation, meteorological and early warning category information service are provided.

Be only specific embodiments of the invention disclosed in above-mentioned, this embodiment is only that clearer explanation the present invention is used, and is not limitation of the invention, and the changes that any person skilled in the art can think of, all should drop in protection domain.

Claims (13)

1. a General Aviation multi-source information supervising platform, is characterized in that, comprises data fusion module, resource management module, database module and human-computer interaction module, wherein:

Described data fusion module comprises data correlation Fusion Module, Study on Trend module and measures of effectiveness module;

Described data correlation Fusion Module, carries out association judgement and data fusion to the track data that some acquisition sensors detect by fuzzy clustering algorithm, draws overall empty feelings information;

Described Study on Trend module, carries out Study on Trend by empty for described entirety feelings information in conjunction with outside integrated information, if the data of noting abnormalities, is recorded in described database module;

Described measures of effectiveness module, carries out running status real time monitoring to described some acquisition sensors, assesses platform and integrally usefulness; By sensor detectivity, sensor antijamming capability, sensing system platform property and sensor anti-low latitude ability are resolved into several component further, then weight calculation is carried out to these components draw platform measures of effectiveness result; Weighing computation method is specific as follows:

According to evaluation module, component is divided, determine that set of factors is: detectivity U ₁{ low clearance area coverage coefficient, key area coverage coefficient, warning region coverage coefficient, guidance field coverage coefficient }; Anti-low latitude ability U ₂{ sensor type, sensor system }; System performance U ₃{ benefit coefficient, system performance, system operating mode, coefficient of frequency }; U ₄antijamming capability { spatial domain overlap coefficient, frequency overlap coefficient, polarization factor, the signal type factor, the signal handling capacity factor, single-sensor antijamming capability }, uses matrix U ₁={ u ₁, u ₂, u ₃, u ₄; U ₂={ u ₅, u ₆; U ₃={ u ₇, u ₈, u ₉, u ₁₀; U ₄={ u ₁₁, u ₁₂, u ₁₃, u ₁₄, u ₁₅, u ₁₆represent;

If U ₁, U ₂, U ₃, U ₄the each self-corresponding weight sets matrix of set of factors is: A ₁={ a ₁₁, a ₁₂, a ₁₃, a ₁₄; A ₂={ a ₂₁, a ₂₂; A ₃={ a ₃₁, a ₃₂, a ₃₃, a ₃₄; A ₄={ a ₄₁, a ₄₂, a ₄₃, a ₄₄, a ₄₅, a ₄₆, and $\underset{j}{\&Sigma;}{a}_{ij}=1,$ i＝1,2,3,4；

If Effectiveness Evaluation result set is { excellent, good, in, better, poor }, with matrix V={ v ₁, v ₂... v ₅represent;

The degree of membership of each component on measures of effectiveness result set V constitutes fuzzy relationship matrix r=(μ (u)) of this evaluation process U to V _{i × J}, wherein μ is fuzzification function, and u is each component, and I is factor number in set of factors, and J is result set result number;

Finally draw Effectiveness Evaluation computing formula: B=A o R=(b ₁, b ₂, b ₃, b ₄, b ₅), b _j(j=1,2 ... 5) larger, illustrate that this system effectiveness is under the jurisdiction of v _jthe degree of Efficacy Results collection is larger;

Described resource management module comprises sensor management module and comprehensive analysis module;

Described sensor management module, completes the condition managing to described some acquisition sensors, condition monitoring and task scheduling;

Described comprehensive analysis module, the association of the track data of comprehensive analysis measures of effectiveness result, the detection of described some acquisition sensor is merged and described some acquisition sensor running state information, forms overall empty feelings information, the coverage information of overall detect effi-ciency and resource health status information;

Described database module, is connected with data fusion module, resource management module and human-computer interaction module, automatically enrolls abnormal data, inquires about and playback;

Described human-computer interaction module, connect described data fusion module and described resource management module, show overall empty feelings information, the coverage information of overall detect effi-ciency and resource health status information, send when receiving abnormal data and threaten report and early warning and by the power range dynamic conditioning of described sensor management module to some acquisition sensors.

2. a kind of General Aviation multi-source information supervising platform as claimed in claim 1, is characterized in that, described human-computer interaction module carries out switching display to the coverage information of entirety empty feelings information, overall detect effi-ciency and resource health status information.

3. a kind of General Aviation multi-source information supervising platform as claimed in claim 1, it is characterized in that, described outside integrated information is weather information, the geography information of outside Geographic Information System collection and the blank pipe information of outside air traffic control system collection that outside weather infosystem gathers.

4. a kind of General Aviation multi-source information supervising platform as claimed in claim 1, is characterized in that, also comprise information service module, empty for entirety feelings information is carried out data processing, provides navigation, meteorological and early warning category information service.

5. a kind of General Aviation multi-source information supervising platform as claimed in claim 1, is characterized in that, the empty feelings information of described entirety comprises target type and target status information.

6. a kind of General Aviation multi-source information supervising platform as claimed in claim 5, it is characterized in that, described target status information comprises flight number, invasion time, the speed of a ship or plane, course, flying height, flying speed and landing event information.

7. a General Aviation multi-source information monitoring and managing method, is characterized in that, comprises the following steps:

By fuzzy clustering algorithm, association judgement and data fusion are carried out to the track data that some acquisition sensors detect, draws overall empty feelings information;

Empty for described entirety feelings information is carried out Study on Trend in conjunction with outside integrated information;

Running status real time monitoring is carried out to described acquisition sensor, platform and integrally usefulness is assessed;

Condition managing, condition monitoring and task scheduling are carried out to described some acquisition sensors;

The association fusion results of the track data of comprehensive analysis measures of effectiveness result, described some acquisition sensor detections and described some acquisition sensor running state information, form usefulness coverage information and the resource health status information of comprehensive empty feelings information and overall detection;

The overall empty feelings information of real-time display, the coverage information of overall detect effi-ciency and resource health status information;

During the abnormal data occurred in platform, abnormal data enrolled automatically, inquire about and playback, and when receiving abnormal data, send threat report and early warning, and the power range dynamic conditioning to some acquisition sensors;

By sensor detectivity, sensor antijamming capability, sensing system platform property and sensor anti-low latitude ability are resolved into several component further, then weight calculation is carried out to these components draw platform measures of effectiveness result; Weighing computation method is specific as follows:

According to evaluation module, component is divided, determine that set of factors is: detectivity U ₁{ low clearance area coverage coefficient, key area coverage coefficient, warning region coverage coefficient, guidance field coverage coefficient }; Anti-low latitude ability U ₂{ sensor type, sensor system }; System performance U ₃{ benefit coefficient, system performance, system operating mode, coefficient of frequency }; U ₄antijamming capability { spatial domain overlap coefficient, frequency overlap coefficient, polarization factor, the signal type factor, the signal handling capacity factor, single-sensor antijamming capability }, uses matrix U ₁={ u ₁, u ₂, u ₃, u ₄; U ₂={ u ₅, u ₆; U ₃={ u ₇, u ₈, u ₉, u ₁₀; U ₄={ u ₁₁, u ₁₂, u ₁₃, u ₁₄, u ₁₅, u ₁₆represent;

If U ₁, U ₂, U ₃, U ₄the each self-corresponding weight sets matrix of set of factors is: A ₁={ a ₁₁, a ₁₂, a ₁₃, a ₁₄; A ₂={ a ₂₁, a ₂₂; A ₃={ a ₃₁, a ₃₂, a ₃₃, a ₃₄; A ₄={ a ₄₁, a ₄₂, a ₄₃, a ₄₄, a ₄₅, a ₄₆, and $\underset{j}{\&Sigma;}{a}_{ij}=1,$ i＝1,2,3,4；

If Effectiveness Evaluation result set is { excellent, good, in, better, poor }, with matrix V={ v ₁, v ₂... v ₅represent;

The degree of membership of each component on measures of effectiveness result set V constitutes fuzzy relationship matrix r=(μ (u)) of this evaluation process U to V _{i × J}, wherein μ is fuzzification function, and u is each component, and I is factor number in set of factors, and J is result set result number;

Finally draw Effectiveness Evaluation computing formula: B=A o R=(b ₁, b ₂, b ₃, b ₄, b ₅), b _j(j=1,2 ... 5) larger, illustrate that this system effectiveness is under the jurisdiction of v _jthe degree of Efficacy Results collection is larger.

8. a kind of General Aviation multi-source information monitoring and managing method as claimed in claim 7, is characterized in that, the step that the track data of detection carries out associating judgement and data fusion also comprises:

Calibrate pre-service when a. the target that each acquisition sensor detects being carried out sky, obtain the tenacious tracking track of each sensor detection information;

B. Registration of Measuring Data is carried out by time stamp alignment so;

C. fuzzy clustering algorithm is adopted to carry out data correlation judgement and data fusion;

D. overall empty feelings information is drawn.

9. a kind of General Aviation multi-source information monitoring and managing method as claimed in claim 7, is characterized in that, the coverage information of the overall empty feelings information of switching display, overall detect effi-ciency and resource health status information.

10. a kind of General Aviation multi-source information monitoring and managing method as claimed in claim 7, it is characterized in that, described outside integrated information is weather information, the geography information of outside Geographic Information System collection and the blank pipe information of outside air traffic control system collection that outside weather infosystem gathers.

11. a kind of General Aviation multi-source information monitoring and managing methods as claimed in claim 7, it is characterized in that, the empty feelings information of described entirety comprises target type and target status information.

12. a kind of General Aviation multi-source information monitoring and managing methods as claimed in claim 11, it is characterized in that, described target status information comprises flight number, invasion time, the speed of a ship or plane, course, flying height and landing event information.

13. a kind of General Aviation multi-source information monitoring and managing methods as claimed in claim 7, is characterized in that, also comprise and empty for entirety feelings information is carried out data processing, provide navigation, meteorological and early warning category information service.